There exists a need to display high resolution terrain data (e.g. Digital Terrain Elevation Data (DTED)) from global to high-resolution scales. To a limited extent this type of task has been accomplished using free distribution applications such as Google Earth™. Specifically, what is needed is a method for loading and managing massive, gridded, datasets of the type available from the National Geospatial Intelligence Agency (NGA) and the US Geological Survey (USGS).
Brute force computing methods for loading or displaying such voluminous data at the global level exceed the current capabilities of modern processing hardware. The only way to achieve the desired visual quality and interactive speed is to use real-time algorithms that vary the quality of the displayed content. Such algorithms would necessarily feature 1) a data structure for storing, manipulating, and processing of vertex information, and 2) intelligent processing of the information contained within the data structure.
The ability to view terrain with such a large number of vertices is an important research area, where several in-core and out-of-core techniques have been developed. However, these prior art techniques require an expensive pre-processing stage which decimates the statistical accuracy of the original incoming data. Therefore, an urgent need exists because the volume and resolution of terrain data is growing faster than our ability to display it.